Overview

Dataset statistics

Number of variables55
Number of observations581012
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory243.8 MiB
Average record size in memory440.0 B

Variable types

Numeric11
Categorical44

Warnings

Elevation is highly correlated with Wilderness_Area4High correlation
Aspect is highly correlated with Hillshade_9am and 1 other fieldsHigh correlation
Slope is highly correlated with Hillshade_NoonHigh correlation
Horizontal_Distance_To_Hydrology is highly correlated with Vertical_Distance_To_HydrologyHigh correlation
Vertical_Distance_To_Hydrology is highly correlated with Horizontal_Distance_To_HydrologyHigh correlation
Hillshade_9am is highly correlated with Aspect and 1 other fieldsHigh correlation
Hillshade_Noon is highly correlated with Slope and 1 other fieldsHigh correlation
Hillshade_3pm is highly correlated with Aspect and 2 other fieldsHigh correlation
Wilderness_Area1 is highly correlated with Wilderness_Area3 and 1 other fieldsHigh correlation
Wilderness_Area3 is highly correlated with Wilderness_Area1High correlation
Wilderness_Area4 is highly correlated with ElevationHigh correlation
Soil_Type29 is highly correlated with Wilderness_Area1High correlation
Aspect is highly correlated with Hillshade_3pmHigh correlation
Horizontal_Distance_To_Hydrology is highly correlated with Vertical_Distance_To_HydrologyHigh correlation
Vertical_Distance_To_Hydrology is highly correlated with Horizontal_Distance_To_HydrologyHigh correlation
Hillshade_9am is highly correlated with Hillshade_3pmHigh correlation
Hillshade_Noon is highly correlated with Hillshade_3pmHigh correlation
Hillshade_3pm is highly correlated with Aspect and 2 other fieldsHigh correlation
Wilderness_Area1 is highly correlated with Wilderness_Area3 and 1 other fieldsHigh correlation
Wilderness_Area3 is highly correlated with Wilderness_Area1High correlation
Soil_Type29 is highly correlated with Wilderness_Area1High correlation
Hillshade_9am is highly correlated with Hillshade_3pmHigh correlation
Hillshade_3pm is highly correlated with Hillshade_9amHigh correlation
Wilderness_Area1 is highly correlated with Wilderness_Area3 and 1 other fieldsHigh correlation
Wilderness_Area3 is highly correlated with Wilderness_Area1High correlation
Soil_Type29 is highly correlated with Wilderness_Area1High correlation
Covertype is highly correlated with Elevation and 1 other fieldsHigh correlation
Wilderness_Area1 is highly correlated with Horizontal_Distance_To_Roadways and 2 other fieldsHigh correlation
Soil_Type38 is highly correlated with ElevationHigh correlation
Hillshade_9am is highly correlated with Hillshade_3pm and 2 other fieldsHigh correlation
Soil_Type40 is highly correlated with ElevationHigh correlation
Soil_Type6 is highly correlated with Wilderness_Area4High correlation
Hillshade_Noon is highly correlated with Hillshade_3pm and 2 other fieldsHigh correlation
Horizontal_Distance_To_Roadways is highly correlated with Wilderness_Area1 and 2 other fieldsHigh correlation
Elevation is highly correlated with Covertype and 5 other fieldsHigh correlation
Horizontal_Distance_To_Hydrology is highly correlated with Vertical_Distance_To_HydrologyHigh correlation
Hillshade_3pm is highly correlated with Hillshade_9am and 3 other fieldsHigh correlation
Soil_Type10 is highly correlated with Elevation and 1 other fieldsHigh correlation
Wilderness_Area3 is highly correlated with Wilderness_Area1 and 2 other fieldsHigh correlation
Aspect is highly correlated with Hillshade_9am and 2 other fieldsHigh correlation
Slope is highly correlated with Hillshade_9am and 2 other fieldsHigh correlation
Vertical_Distance_To_Hydrology is highly correlated with Horizontal_Distance_To_HydrologyHigh correlation
Wilderness_Area4 is highly correlated with Covertype and 3 other fieldsHigh correlation
Soil_Type29 is highly correlated with Wilderness_Area1 and 1 other fieldsHigh correlation
Wilderness_Area1 is highly correlated with Wilderness_Area3 and 1 other fieldsHigh correlation
Wilderness_Area3 is highly correlated with Wilderness_Area1High correlation
Soil_Type29 is highly correlated with Wilderness_Area1High correlation
Horizontal_Distance_To_Hydrology has 24603 (4.2%) zeros Zeros
Vertical_Distance_To_Hydrology has 38665 (6.7%) zeros Zeros

Reproduction

Analysis started2021-08-26 15:03:51.857290
Analysis finished2021-08-26 15:08:23.885153
Duration4 minutes and 32.03 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Elevation
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1978
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2959.365301
Minimum1859
Maximum3858
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2021-08-26T18:08:23.935270image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1859
5-th percentile2406
Q12809
median2996
Q33163
95-th percentile3336
Maximum3858
Range1999
Interquartile range (IQR)354

Descriptive statistics

Standard deviation279.9847343
Coefficient of variation (CV)0.09460972398
Kurtosis0.7492507754
Mean2959.365301
Median Absolute Deviation (MAD)175
Skewness-0.8175958183
Sum1719426752
Variance78391.45141
MonotonicityNot monotonic
2021-08-26T18:08:24.036433image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29681681
 
0.3%
29621674
 
0.3%
29911671
 
0.3%
29721662
 
0.3%
29781656
 
0.3%
29751656
 
0.3%
29881619
 
0.3%
29551590
 
0.3%
29521577
 
0.3%
29651571
 
0.3%
Other values (1968)564655
97.2%
ValueCountFrequency (%)
18591
 
< 0.1%
18601
 
< 0.1%
18611
 
< 0.1%
18631
 
< 0.1%
18661
 
< 0.1%
18671
 
< 0.1%
18681
 
< 0.1%
18713
< 0.1%
18724
< 0.1%
18731
 
< 0.1%
ValueCountFrequency (%)
38582
 
< 0.1%
38571
 
< 0.1%
38561
 
< 0.1%
38531
 
< 0.1%
38521
 
< 0.1%
38512
 
< 0.1%
38501
 
< 0.1%
38494
< 0.1%
38481
 
< 0.1%
38466
< 0.1%

Aspect
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct361
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.6568074
Minimum0
Maximum360
Zeros4914
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2021-08-26T18:08:24.276519image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q158
median127
Q3260
95-th percentile344
Maximum360
Range360
Interquartile range (IQR)202

Descriptive statistics

Standard deviation111.913721
Coefficient of variation (CV)0.7189773634
Kurtosis-1.220238943
Mean155.6568074
Median Absolute Deviation (MAD)85
Skewness0.4026283208
Sum90438473
Variance12524.68095
MonotonicityNot monotonic
2021-08-26T18:08:24.376689image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
456308
 
1.1%
04914
 
0.8%
904677
 
0.8%
1353834
 
0.7%
633680
 
0.6%
3153574
 
0.6%
723407
 
0.6%
183403
 
0.6%
273392
 
0.6%
342836
 
0.5%
Other values (351)540987
93.1%
ValueCountFrequency (%)
04914
0.8%
11671
 
0.3%
21902
 
0.3%
31945
 
0.3%
42267
0.4%
52063
0.4%
62242
0.4%
72194
0.4%
82213
0.4%
92460
0.4%
ValueCountFrequency (%)
36051
 
< 0.1%
3591407
0.2%
3581749
0.3%
3571860
0.3%
3562025
0.3%
3551933
0.3%
3542025
0.3%
3531946
0.3%
3521985
0.3%
3512184
0.4%

Slope
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct67
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.10370354
Minimum0
Maximum66
Zeros656
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2021-08-26T18:08:24.479876image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q19
median13
Q318
95-th percentile28
Maximum66
Range66
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.488241814
Coefficient of variation (CV)0.5309415214
Kurtosis0.58119911
Mean14.10370354
Median Absolute Deviation (MAD)5
Skewness0.7892725472
Sum8194421
Variance56.07376547
MonotonicityNot monotonic
2021-08-26T18:08:24.575604image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1133824
 
5.8%
1033812
 
5.8%
1233217
 
5.7%
1332419
 
5.6%
932049
 
5.5%
1430282
 
5.2%
830130
 
5.2%
1529127
 
5.0%
1626541
 
4.6%
726395
 
4.5%
Other values (57)273216
47.0%
ValueCountFrequency (%)
0656
 
0.1%
13680
 
0.6%
27726
 
1.3%
311620
 
2.0%
416344
2.8%
520810
3.6%
624504
4.2%
726395
4.5%
830130
5.2%
932049
5.5%
ValueCountFrequency (%)
661
 
< 0.1%
652
 
< 0.1%
641
 
< 0.1%
631
 
< 0.1%
622
 
< 0.1%
614
< 0.1%
602
 
< 0.1%
593
< 0.1%
581
 
< 0.1%
577
< 0.1%

Horizontal_Distance_To_Hydrology
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct551
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean269.4282166
Minimum0
Maximum1397
Zeros24603
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2021-08-26T18:08:24.673816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1108
median218
Q3384
95-th percentile684
Maximum1397
Range1397
Interquartile range (IQR)276

Descriptive statistics

Standard deviation212.5493556
Coefficient of variation (CV)0.7888904817
Kurtosis1.366180499
Mean269.4282166
Median Absolute Deviation (MAD)133
Skewness1.140437392
Sum156541027
Variance45177.22856
MonotonicityNot monotonic
2021-08-26T18:08:24.768530image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3034139
 
5.9%
024603
 
4.2%
15020785
 
3.6%
6019189
 
3.3%
6715223
 
2.6%
4214647
 
2.5%
10814358
 
2.5%
8513741
 
2.4%
9011140
 
1.9%
12010673
 
1.8%
Other values (541)402514
69.3%
ValueCountFrequency (%)
024603
4.2%
3034139
5.9%
4214647
2.5%
6019189
3.3%
6715223
2.6%
8513741
2.4%
9011140
 
1.9%
959216
 
1.6%
10814358
2.5%
12010673
 
1.8%
ValueCountFrequency (%)
13971
< 0.1%
13902
< 0.1%
13832
< 0.1%
13821
< 0.1%
13761
< 0.1%
13711
< 0.1%
13701
< 0.1%
13691
< 0.1%
13682
< 0.1%
13612
< 0.1%

Vertical_Distance_To_Hydrology
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct700
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.41885538
Minimum-173
Maximum601
Zeros38665
Zeros (%)6.7%
Negative55143
Negative (%)9.5%
Memory size4.4 MiB
2021-08-26T18:08:24.870233image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-173
5-th percentile-8
Q17
median30
Q369
95-th percentile165
Maximum601
Range774
Interquartile range (IQR)62

Descriptive statistics

Standard deviation58.29523163
Coefficient of variation (CV)1.255852415
Kurtosis5.250295781
Mean46.41885538
Median Absolute Deviation (MAD)27
Skewness1.790249746
Sum26969912
Variance3398.33403
MonotonicityNot monotonic
2021-08-26T18:08:24.971416image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
038665
 
6.7%
39298
 
1.6%
108863
 
1.5%
78741
 
1.5%
68590
 
1.5%
138474
 
1.5%
48397
 
1.4%
57614
 
1.3%
167429
 
1.3%
97331
 
1.3%
Other values (690)467610
80.5%
ValueCountFrequency (%)
-1731
 
< 0.1%
-1662
< 0.1%
-1641
 
< 0.1%
-1631
 
< 0.1%
-1611
 
< 0.1%
-1593
< 0.1%
-1581
 
< 0.1%
-1572
< 0.1%
-1562
< 0.1%
-1553
< 0.1%
ValueCountFrequency (%)
6011
 
< 0.1%
5991
 
< 0.1%
5982
< 0.1%
5973
< 0.1%
5952
< 0.1%
5921
 
< 0.1%
5911
 
< 0.1%
5902
< 0.1%
5893
< 0.1%
5883
< 0.1%

Horizontal_Distance_To_Roadways
Real number (ℝ≥0)

HIGH CORRELATION

Distinct5785
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2350.146611
Minimum0
Maximum7117
Zeros124
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2021-08-26T18:08:25.075076image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile379
Q11106
median1997
Q33328
95-th percentile5483
Maximum7117
Range7117
Interquartile range (IQR)2222

Descriptive statistics

Standard deviation1559.25487
Coefficient of variation (CV)0.6634713181
Kurtosis-0.3837111912
Mean2350.146611
Median Absolute Deviation (MAD)1040
Skewness0.7136788231
Sum1365463383
Variance2431275.749
MonotonicityNot monotonic
2021-08-26T18:08:25.171304image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1501332
 
0.2%
6181065
 
0.2%
900918
 
0.2%
390914
 
0.2%
1020900
 
0.2%
990878
 
0.2%
960868
 
0.1%
997859
 
0.1%
750847
 
0.1%
1140840
 
0.1%
Other values (5775)571591
98.4%
ValueCountFrequency (%)
0124
 
< 0.1%
30313
0.1%
42171
 
< 0.1%
60312
0.1%
67298
0.1%
85384
0.1%
90380
0.1%
95374
0.1%
108660
0.1%
120633
0.1%
ValueCountFrequency (%)
71171
< 0.1%
71161
< 0.1%
71121
< 0.1%
70971
< 0.1%
70921
< 0.1%
70872
< 0.1%
70821
< 0.1%
70791
< 0.1%
70782
< 0.1%
70691
< 0.1%

Hillshade_9am
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct207
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.1460486
Minimum0
Maximum254
Zeros13
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2021-08-26T18:08:25.270009image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile160
Q1198
median218
Q3231
95-th percentile246
Maximum254
Range254
Interquartile range (IQR)33

Descriptive statistics

Standard deviation26.76988881
Coefficient of variation (CV)0.1261861297
Kurtosis1.875517665
Mean212.1460486
Median Absolute Deviation (MAD)16
Skewness-1.181146663
Sum123259400
Variance716.6269466
MonotonicityNot monotonic
2021-08-26T18:08:25.369207image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22611657
 
2.0%
22811374
 
2.0%
23011355
 
2.0%
22411210
 
1.9%
22310887
 
1.9%
22210809
 
1.9%
23310645
 
1.8%
22710513
 
1.8%
22510307
 
1.8%
22110264
 
1.8%
Other values (197)471991
81.2%
ValueCountFrequency (%)
013
< 0.1%
361
 
< 0.1%
462
 
< 0.1%
501
 
< 0.1%
522
 
< 0.1%
531
 
< 0.1%
544
 
< 0.1%
551
 
< 0.1%
566
< 0.1%
572
 
< 0.1%
ValueCountFrequency (%)
2541898
 
0.3%
2532236
0.4%
2522563
0.4%
2512968
0.5%
2503341
0.6%
2493793
0.7%
2483955
0.7%
2474443
0.8%
2465008
0.9%
2455530
1.0%

Hillshade_Noon
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct185
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean223.3187163
Minimum0
Maximum254
Zeros5
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2021-08-26T18:08:25.464436image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile186
Q1213
median226
Q3237
95-th percentile250
Maximum254
Range254
Interquartile range (IQR)24

Descriptive statistics

Standard deviation19.76869715
Coefficient of variation (CV)0.08852234815
Kurtosis2.066210843
Mean223.3187163
Median Absolute Deviation (MAD)12
Skewness-1.063056257
Sum129750854
Variance390.8013872
MonotonicityNot monotonic
2021-08-26T18:08:25.565128image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22813696
 
2.4%
23113666
 
2.4%
23313297
 
2.3%
22913271
 
2.3%
23013258
 
2.3%
23413047
 
2.2%
22713020
 
2.2%
22312989
 
2.2%
22612953
 
2.2%
22512928
 
2.2%
Other values (175)448887
77.3%
ValueCountFrequency (%)
05
< 0.1%
301
 
< 0.1%
401
 
< 0.1%
421
 
< 0.1%
451
 
< 0.1%
532
 
< 0.1%
631
 
< 0.1%
641
 
< 0.1%
681
 
< 0.1%
711
 
< 0.1%
ValueCountFrequency (%)
2545902
1.0%
2536300
1.1%
2527171
1.2%
2517471
1.3%
2508028
1.4%
2497714
1.3%
2488133
1.4%
2478874
1.5%
2468665
1.5%
2458538
1.5%

Hillshade_3pm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct255
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.5282628
Minimum0
Maximum254
Zeros1338
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2021-08-26T18:08:25.661328image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile78
Q1119
median143
Q3168
95-th percentile204
Maximum254
Range254
Interquartile range (IQR)49

Descriptive statistics

Standard deviation38.27452923
Coefficient of variation (CV)0.2685399267
Kurtosis0.3984400114
Mean142.5282628
Median Absolute Deviation (MAD)25
Skewness-0.2770531973
Sum82810631
Variance1464.939588
MonotonicityNot monotonic
2021-08-26T18:08:25.763033image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1437333
 
1.3%
1457217
 
1.2%
1387065
 
1.2%
1466915
 
1.2%
1426902
 
1.2%
1366871
 
1.2%
1396858
 
1.2%
1356781
 
1.2%
1496723
 
1.2%
1326673
 
1.1%
Other values (245)511674
88.1%
ValueCountFrequency (%)
01338
0.2%
115
 
< 0.1%
215
 
< 0.1%
315
 
< 0.1%
420
 
< 0.1%
518
 
< 0.1%
626
 
< 0.1%
730
 
< 0.1%
821
 
< 0.1%
933
 
< 0.1%
ValueCountFrequency (%)
2544
 
< 0.1%
2538
 
< 0.1%
25216
 
< 0.1%
25111
 
< 0.1%
25017
 
< 0.1%
24937
< 0.1%
24844
< 0.1%
24761
< 0.1%
24672
< 0.1%
24585
< 0.1%
Distinct5827
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1980.291226
Minimum0
Maximum7173
Zeros51
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2021-08-26T18:08:25.862231image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile418
Q11024
median1710
Q32550
95-th percentile4944
Maximum7173
Range7173
Interquartile range (IQR)1526

Descriptive statistics

Standard deviation1324.19521
Coefficient of variation (CV)0.6686871063
Kurtosis1.64580685
Mean1980.291226
Median Absolute Deviation (MAD)750
Skewness1.288644077
Sum1150572966
Variance1753492.954
MonotonicityNot monotonic
2021-08-26T18:08:25.958953image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6181412
 
0.2%
5411099
 
0.2%
6071054
 
0.2%
9421023
 
0.2%
9971004
 
0.2%
700958
 
0.2%
900937
 
0.2%
726923
 
0.2%
752910
 
0.2%
960908
 
0.2%
Other values (5817)570784
98.2%
ValueCountFrequency (%)
051
 
< 0.1%
30206
< 0.1%
42207
< 0.1%
60206
< 0.1%
67416
0.1%
85207
< 0.1%
90204
< 0.1%
95412
0.1%
108412
0.1%
120204
< 0.1%
ValueCountFrequency (%)
71731
< 0.1%
71721
< 0.1%
71681
< 0.1%
71501
< 0.1%
71451
< 0.1%
71421
< 0.1%
71412
< 0.1%
71401
< 0.1%
71311
< 0.1%
71261
< 0.1%

Wilderness_Area1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
320216 
1
260796 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0320216
55.1%
1260796
44.9%

Length

2021-08-26T18:08:26.118662image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:26.155343image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0320216
55.1%
1260796
44.9%

Most occurring characters

ValueCountFrequency (%)
0320216
55.1%
1260796
44.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0320216
55.1%
1260796
44.9%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0320216
55.1%
1260796
44.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0320216
55.1%
1260796
44.9%

Wilderness_Area2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
551128 
1
 
29884

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0551128
94.9%
129884
 
5.1%

Length

2021-08-26T18:08:26.453440image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:26.499590image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0551128
94.9%
129884
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0551128
94.9%
129884
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0551128
94.9%
129884
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0551128
94.9%
129884
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0551128
94.9%
129884
 
5.1%

Wilderness_Area3
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
327648 
1
253364 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0327648
56.4%
1253364
43.6%

Length

2021-08-26T18:08:26.625576image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:26.671702image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0327648
56.4%
1253364
43.6%

Most occurring characters

ValueCountFrequency (%)
0327648
56.4%
1253364
43.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0327648
56.4%
1253364
43.6%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0327648
56.4%
1253364
43.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0327648
56.4%
1253364
43.6%

Wilderness_Area4
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
544044 
1
 
36968

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0544044
93.6%
136968
 
6.4%

Length

2021-08-26T18:08:26.784296image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:26.830421image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0544044
93.6%
136968
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0544044
93.6%
136968
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0544044
93.6%
136968
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0544044
93.6%
136968
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0544044
93.6%
136968
 
6.4%

Soil_Type1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
577981 
1
 
3031

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0577981
99.5%
13031
 
0.5%

Length

2021-08-26T18:08:26.949958image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:26.996085image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0577981
99.5%
13031
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0577981
99.5%
13031
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0577981
99.5%
13031
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0577981
99.5%
13031
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0577981
99.5%
13031
 
0.5%

Soil_Type2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
573487 
1
 
7525

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0573487
98.7%
17525
 
1.3%

Length

2021-08-26T18:08:27.114133image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:27.160262image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0573487
98.7%
17525
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0573487
98.7%
17525
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0573487
98.7%
17525
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0573487
98.7%
17525
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0573487
98.7%
17525
 
1.3%

Soil_Type3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
576189 
1
 
4823

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0576189
99.2%
14823
 
0.8%

Length

2021-08-26T18:08:27.278805image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:27.324934image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0576189
99.2%
14823
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0576189
99.2%
14823
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0576189
99.2%
14823
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0576189
99.2%
14823
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0576189
99.2%
14823
 
0.8%

Soil_Type4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
568616 
1
 
12396

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0568616
97.9%
112396
 
2.1%

Length

2021-08-26T18:08:27.440005image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:27.486134image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0568616
97.9%
112396
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0568616
97.9%
112396
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0568616
97.9%
112396
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0568616
97.9%
112396
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0568616
97.9%
112396
 
2.1%

Soil_Type5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
579415 
1
 
1597

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0579415
99.7%
11597
 
0.3%

Length

2021-08-26T18:08:27.604679image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:27.652293image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0579415
99.7%
11597
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0579415
99.7%
11597
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0579415
99.7%
11597
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0579415
99.7%
11597
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0579415
99.7%
11597
 
0.3%

Soil_Type6
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
574437 
1
 
6575

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0574437
98.9%
16575
 
1.1%

Length

2021-08-26T18:08:27.942431image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:27.988581image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0574437
98.9%
16575
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0574437
98.9%
16575
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0574437
98.9%
16575
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0574437
98.9%
16575
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0574437
98.9%
16575
 
1.1%

Soil_Type7
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
580907 
1
 
105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0580907
> 99.9%
1105
 
< 0.1%

Length

2021-08-26T18:08:28.108615image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:28.154223image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0580907
> 99.9%
1105
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0580907
> 99.9%
1105
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0580907
> 99.9%
1105
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0580907
> 99.9%
1105
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0580907
> 99.9%
1105
 
< 0.1%

Soil_Type8
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
580833 
1
 
179

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0580833
> 99.9%
1179
 
< 0.1%

Length

2021-08-26T18:08:28.272766image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:28.318917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0580833
> 99.9%
1179
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0580833
> 99.9%
1179
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0580833
> 99.9%
1179
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0580833
> 99.9%
1179
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0580833
> 99.9%
1179
 
< 0.1%

Soil_Type9
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
579865 
1
 
1147

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0579865
99.8%
11147
 
0.2%

Length

2021-08-26T18:08:28.437461image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:28.483590image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0579865
99.8%
11147
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0579865
99.8%
11147
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0579865
99.8%
11147
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0579865
99.8%
11147
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0579865
99.8%
11147
 
0.2%

Soil_Type10
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
548378 
1
 
32634

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0548378
94.4%
132634
 
5.6%

Length

2021-08-26T18:08:28.596180image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:28.642309image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0548378
94.4%
132634
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0548378
94.4%
132634
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0548378
94.4%
132634
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0548378
94.4%
132634
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0548378
94.4%
132634
 
5.6%

Soil_Type11
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
568602 
1
 
12410

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0568602
97.9%
112410
 
2.1%

Length

2021-08-26T18:08:28.757876image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:28.803508image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0568602
97.9%
112410
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0568602
97.9%
112410
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0568602
97.9%
112410
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0568602
97.9%
112410
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0568602
97.9%
112410
 
2.1%

Soil_Type12
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
551041 
1
 
29971

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0551041
94.8%
129971
 
5.2%

Length

2021-08-26T18:08:28.916101image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:28.962229image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0551041
94.8%
129971
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0551041
94.8%
129971
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0551041
94.8%
129971
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0551041
94.8%
129971
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0551041
94.8%
129971
 
5.2%

Soil_Type13
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
563581 
1
 
17431

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0563581
97.0%
117431
 
3.0%

Length

2021-08-26T18:08:29.077805image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:29.123428image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0563581
97.0%
117431
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0563581
97.0%
117431
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0563581
97.0%
117431
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0563581
97.0%
117431
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0563581
97.0%
117431
 
3.0%

Soil_Type14
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
580413 
1
 
599

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0580413
99.9%
1599
 
0.1%

Length

2021-08-26T18:08:29.242468image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:29.289092image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0580413
99.9%
1599
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0580413
99.9%
1599
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0580413
99.9%
1599
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0580413
99.9%
1599
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0580413
99.9%
1599
 
0.1%

Soil_Type15
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
581009 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0581009
> 99.9%
13
 
< 0.1%

Length

2021-08-26T18:08:29.405156image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:29.449796image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0581009
> 99.9%
13
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0581009
> 99.9%
13
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0581009
> 99.9%
13
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0581009
> 99.9%
13
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0581009
> 99.9%
13
 
< 0.1%

Soil_Type16
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
578167 
1
 
2845

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0578167
99.5%
12845
 
0.5%

Length

2021-08-26T18:08:29.565860image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:29.610973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0578167
99.5%
12845
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0578167
99.5%
12845
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0578167
99.5%
12845
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0578167
99.5%
12845
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0578167
99.5%
12845
 
0.5%

Soil_Type17
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
577590 
1
 
3422

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0577590
99.4%
13422
 
0.6%

Length

2021-08-26T18:08:29.726564image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:29.771700image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0577590
99.4%
13422
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0577590
99.4%
13422
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0577590
99.4%
13422
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0577590
99.4%
13422
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0577590
99.4%
13422
 
0.6%

Soil_Type18
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
579113 
1
 
1899

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0579113
99.7%
11899
 
0.3%

Length

2021-08-26T18:08:29.887762image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:29.932900image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0579113
99.7%
11899
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0579113
99.7%
11899
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0579113
99.7%
11899
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0579113
99.7%
11899
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0579113
99.7%
11899
 
0.3%

Soil_Type19
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
576991 
1
 
4021

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0576991
99.3%
14021
 
0.7%

Length

2021-08-26T18:08:30.048940image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:30.094596image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0576991
99.3%
14021
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0576991
99.3%
14021
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0576991
99.3%
14021
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0576991
99.3%
14021
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0576991
99.3%
14021
 
0.7%

Soil_Type20
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
571753 
1
 
9259

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0571753
98.4%
19259
 
1.6%

Length

2021-08-26T18:08:30.207190image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:30.251806image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0571753
98.4%
19259
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0571753
98.4%
19259
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0571753
98.4%
19259
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0571753
98.4%
19259
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0571753
98.4%
19259
 
1.6%

Soil_Type21
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
580174 
1
 
838

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0580174
99.9%
1838
 
0.1%

Length

2021-08-26T18:08:30.367891image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:30.413027image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0580174
99.9%
1838
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0580174
99.9%
1838
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0580174
99.9%
1838
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0580174
99.9%
1838
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0580174
99.9%
1838
 
0.1%

Soil_Type22
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
547639 
1
 
33373

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0547639
94.3%
133373
 
5.7%

Length

2021-08-26T18:08:30.523631image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:30.568274image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0547639
94.3%
133373
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0547639
94.3%
133373
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0547639
94.3%
133373
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0547639
94.3%
133373
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0547639
94.3%
133373
 
5.7%

Soil_Type23
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
523260 
1
57752 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0523260
90.1%
157752
 
9.9%

Length

2021-08-26T18:08:30.678879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:30.723997image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0523260
90.1%
157752
 
9.9%

Most occurring characters

ValueCountFrequency (%)
0523260
90.1%
157752
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0523260
90.1%
157752
 
9.9%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0523260
90.1%
157752
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0523260
90.1%
157752
 
9.9%

Soil_Type24
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
559734 
1
 
21278

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0559734
96.3%
121278
 
3.7%

Length

2021-08-26T18:08:30.836607image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:30.881745image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0559734
96.3%
121278
 
3.7%

Most occurring characters

ValueCountFrequency (%)
0559734
96.3%
121278
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0559734
96.3%
121278
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0559734
96.3%
121278
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0559734
96.3%
121278
 
3.7%

Soil_Type25
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
580538 
1
 
474

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0580538
99.9%
1474
 
0.1%

Length

2021-08-26T18:08:30.997811image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:31.042947image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0580538
99.9%
1474
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0580538
99.9%
1474
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0580538
99.9%
1474
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0580538
99.9%
1474
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0580538
99.9%
1474
 
0.1%

Soil_Type26
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
578423 
1
 
2589

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0578423
99.6%
12589
 
0.4%

Length

2021-08-26T18:08:31.159011image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:31.204147image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0578423
99.6%
12589
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0578423
99.6%
12589
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0578423
99.6%
12589
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0578423
99.6%
12589
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0578423
99.6%
12589
 
0.4%

Soil_Type27
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
579926 
1
 
1086

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0579926
99.8%
11086
 
0.2%

Length

2021-08-26T18:08:31.320210image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:31.365347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0579926
99.8%
11086
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0579926
99.8%
11086
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0579926
99.8%
11086
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0579926
99.8%
11086
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0579926
99.8%
11086
 
0.2%

Soil_Type28
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
580066 
1
 
946

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0580066
99.8%
1946
 
0.2%

Length

2021-08-26T18:08:31.481410image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:31.526053image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0580066
99.8%
1946
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0580066
99.8%
1946
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0580066
99.8%
1946
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0580066
99.8%
1946
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0580066
99.8%
1946
 
0.2%

Soil_Type29
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
465765 
1
115247 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0465765
80.2%
1115247
 
19.8%

Length

2021-08-26T18:08:31.646579image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:31.691218image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0465765
80.2%
1115247
 
19.8%

Most occurring characters

ValueCountFrequency (%)
0465765
80.2%
1115247
 
19.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0465765
80.2%
1115247
 
19.8%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0465765
80.2%
1115247
 
19.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0465765
80.2%
1115247
 
19.8%

Soil_Type30
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
550842 
1
 
30170

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0550842
94.8%
130170
 
5.2%

Length

2021-08-26T18:08:31.801827image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:31.846466image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0550842
94.8%
130170
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0550842
94.8%
130170
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0550842
94.8%
130170
 
5.2%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0550842
94.8%
130170
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0550842
94.8%
130170
 
5.2%

Soil_Type31
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
555346 
1
 
25666

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0555346
95.6%
125666
 
4.4%

Length

2021-08-26T18:08:31.959554image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:32.213009image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0555346
95.6%
125666
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0555346
95.6%
125666
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0555346
95.6%
125666
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0555346
95.6%
125666
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0555346
95.6%
125666
 
4.4%

Soil_Type32
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
528493 
1
 
52519

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0528493
91.0%
152519
 
9.0%

Length

2021-08-26T18:08:32.323620image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:32.368755image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0528493
91.0%
152519
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0528493
91.0%
152519
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0528493
91.0%
152519
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0528493
91.0%
152519
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0528493
91.0%
152519
 
9.0%

Soil_Type33
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
535858 
1
 
45154

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0535858
92.2%
145154
 
7.8%

Length

2021-08-26T18:08:32.478865image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:32.523505image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0535858
92.2%
145154
 
7.8%

Most occurring characters

ValueCountFrequency (%)
0535858
92.2%
145154
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0535858
92.2%
145154
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0535858
92.2%
145154
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0535858
92.2%
145154
 
7.8%

Soil_Type34
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
579401 
1
 
1611

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0579401
99.7%
11611
 
0.3%

Length

2021-08-26T18:08:32.639570image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:32.684708image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0579401
99.7%
11611
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0579401
99.7%
11611
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0579401
99.7%
11611
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0579401
99.7%
11611
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0579401
99.7%
11611
 
0.3%

Soil_Type35
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
579121 
1
 
1891

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0579121
99.7%
11891
 
0.3%

Length

2021-08-26T18:08:32.800747image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:32.845409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0579121
99.7%
11891
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0579121
99.7%
11891
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0579121
99.7%
11891
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0579121
99.7%
11891
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0579121
99.7%
11891
 
0.3%

Soil_Type36
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
580893 
1
 
119

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0580893
> 99.9%
1119
 
< 0.1%

Length

2021-08-26T18:08:32.961473image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:33.006116image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0580893
> 99.9%
1119
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0580893
> 99.9%
1119
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0580893
> 99.9%
1119
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0580893
> 99.9%
1119
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0580893
> 99.9%
1119
 
< 0.1%

Soil_Type37
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
580714 
1
 
298

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0580714
99.9%
1298
 
0.1%

Length

2021-08-26T18:08:33.122674image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:33.167313image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0580714
99.9%
1298
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0580714
99.9%
1298
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0580714
99.9%
1298
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0580714
99.9%
1298
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0580714
99.9%
1298
 
0.1%

Soil_Type38
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
565439 
1
 
15573

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0565439
97.3%
115573
 
2.7%

Length

2021-08-26T18:08:33.280401image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:33.325537image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0565439
97.3%
115573
 
2.7%

Most occurring characters

ValueCountFrequency (%)
0565439
97.3%
115573
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0565439
97.3%
115573
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0565439
97.3%
115573
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0565439
97.3%
115573
 
2.7%

Soil_Type39
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
567206 
1
 
13806

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0567206
97.6%
113806
 
2.4%

Length

2021-08-26T18:08:33.438136image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:33.483265image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0567206
97.6%
113806
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0567206
97.6%
113806
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0567206
97.6%
113806
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0567206
97.6%
113806
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0567206
97.6%
113806
 
2.4%

Soil_Type40
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
572262 
1
 
8750

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581012
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0572262
98.5%
18750
 
1.5%

Length

2021-08-26T18:08:33.599307image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-26T18:08:33.643969image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0572262
98.5%
18750
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0572262
98.5%
18750
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number581012
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0572262
98.5%
18750
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common581012
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0572262
98.5%
18750
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII581012
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0572262
98.5%
18750
 
1.5%

Covertype
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.051470538
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2021-08-26T18:08:33.682635image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q32
95-th percentile6
Maximum7
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.396504316
Coefficient of variation (CV)0.6807333035
Kurtosis4.948965205
Mean2.051470538
Median Absolute Deviation (MAD)1
Skewness2.276573673
Sum1191929
Variance1.950224305
MonotonicityNot monotonic
2021-08-26T18:08:33.740200image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2283301
48.8%
1211840
36.5%
335754
 
6.2%
720510
 
3.5%
617367
 
3.0%
59493
 
1.6%
42747
 
0.5%
ValueCountFrequency (%)
1211840
36.5%
2283301
48.8%
335754
 
6.2%
42747
 
0.5%
59493
 
1.6%
617367
 
3.0%
720510
 
3.5%
ValueCountFrequency (%)
720510
 
3.5%
617367
 
3.0%
59493
 
1.6%
42747
 
0.5%
335754
 
6.2%
2283301
48.8%
1211840
36.5%

Interactions

2021-08-26T18:07:55.934109image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:07:56.141934image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:07:56.349260image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:07:56.549623image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:07:56.757941image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:07:56.961820image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:07:57.181052image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:07:57.385899image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:07:57.592737image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:07:57.789147image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:07:57.984075image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:07:58.184955image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:07:58.392282image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:07:58.600603image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:07:58.802970image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:07:59.009306image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:07:59.214652image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:07:59.425450image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:07:59.629802image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:07:59.836138image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:00.034545image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:00.231453image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:00.510699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:00.717534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:00.921385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:01.119789image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:01.323144image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:01.526009image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:01.739782image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:01.940663image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:02.145514image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:02.340936image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:02.535368image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:02.736252image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:02.945576image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:03.155367image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:03.355233image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:03.561596image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:03.769913image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:03.979722image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:04.183078image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:04.389889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:04.589281image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:04.787206image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:04.986102image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:05.197872image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:05.408694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:05.614015image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:05.821366image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:06.028197image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:06.240981image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:06.540565image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:06.757317image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:06.958196image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:07.157094image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:07.357973image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:07.567284image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:07.777070image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:07.978468image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:08.183315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:08.384694image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:08.593507image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:08.796371image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:09.002707image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:09.199619image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:09.396035image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:09.597411image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:09.802258image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:10.006610image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:10.203522image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:10.404898image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:10.604785image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:10.808124image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:11.005553image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:11.206438image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:11.399873image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:11.591307image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:11.785744image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:11.989618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:12.193947image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:12.389889image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:12.590768image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:12.801050image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:13.009865image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:13.205308image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:13.405196image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:13.596653image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:13.787612image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:14.090671image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:14.294527image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:14.498879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:14.697776image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:14.897662image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:15.097057image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:15.300910image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:15.497822image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:15.695704image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:15.888177image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:16.078643image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:16.268582image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:16.475437image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:16.688696image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:16.888606image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:17.091943image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:17.294334image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:17.502653image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:17.705518image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:17.911334image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:18.106285image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:18.301709image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:18.499124image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:18.701976image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:18.904326image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:19.099770image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:19.300132image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:19.498059image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:19.702408image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:19.899328image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:20.099211image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:20.290667image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-26T18:08:20.478651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-08-26T18:08:33.877067image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-26T18:08:34.611641image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-26T18:08:35.356138image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-26T18:08:36.100159image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-26T18:08:36.830767image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-26T18:08:21.594131image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

ElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsWilderness_Area1Wilderness_Area2Wilderness_Area3Wilderness_Area4Soil_Type1Soil_Type2Soil_Type3Soil_Type4Soil_Type5Soil_Type6Soil_Type7Soil_Type8Soil_Type9Soil_Type10Soil_Type11Soil_Type12Soil_Type13Soil_Type14Soil_Type15Soil_Type16Soil_Type17Soil_Type18Soil_Type19Soil_Type20Soil_Type21Soil_Type22Soil_Type23Soil_Type24Soil_Type25Soil_Type26Soil_Type27Soil_Type28Soil_Type29Soil_Type30Soil_Type31Soil_Type32Soil_Type33Soil_Type34Soil_Type35Soil_Type36Soil_Type37Soil_Type38Soil_Type39Soil_Type40Covertype
0259651325805102212321486279100000000000000000000000000000001000000000005
12590562212-63902202351516225100000000000000000000000000000001000000000005
2280413992686531802342381356121100000000000000100000000000000000000000000002
327851551824211830902382381226211100000000000000000000000000000000100000000002
42595452153-13912202341506172100000000000000000000000000000001000000000005
525791326300-15672302371406031100000000000000000000000000000001000000000002
6260645727056332222251386256100000000000000000000000000000001000000000005
7260549423475732222301446228100000000000000000000000000000001000000000005
82617459240566662232211336244100000000000000000000000000000001000000000005
926125910247116362282191246230100000000000000000000000000000001000000000005

Last rows

ElevationAspectSlopeHorizontal_Distance_To_HydrologyVertical_Distance_To_HydrologyHorizontal_Distance_To_RoadwaysHillshade_9amHillshade_NoonHillshade_3pmHorizontal_Distance_To_Fire_PointsWilderness_Area1Wilderness_Area2Wilderness_Area3Wilderness_Area4Soil_Type1Soil_Type2Soil_Type3Soil_Type4Soil_Type5Soil_Type6Soil_Type7Soil_Type8Soil_Type9Soil_Type10Soil_Type11Soil_Type12Soil_Type13Soil_Type14Soil_Type15Soil_Type16Soil_Type17Soil_Type18Soil_Type19Soil_Type20Soil_Type21Soil_Type22Soil_Type23Soil_Type24Soil_Type25Soil_Type26Soil_Type27Soil_Type28Soil_Type29Soil_Type30Soil_Type31Soil_Type32Soil_Type33Soil_Type34Soil_Type35Soil_Type36Soil_Type37Soil_Type38Soil_Type39Soil_Type40Covertype
58100224191682510833124230240126812001001000000000000000000000000000000000000003
5810032415161259529120236237116815001001000000000000000000000000000000000000003
5810042410158249024120238236115819001001000000000000000000000000000000000000003
5810052405159229019120237238119824001001000000000000000000000000000000000000003
5810062401157219015120238238119830001001000000000000000000000000000000000000003
5810072396153208517108240237118837001001000000000000000000000000000000000000003
581008239115219671295240237119845001001000000000000000000000000000000000000003
58100923861591760790236241130854001001000000000000000000000000000000000000003
58101023841701560590230245143864001001000000000000000000000000000000000000003
58101123831651360467231244141875001001000000000000000000000000000000000000003